Multi-Scale hierarchical generation of PET parametric maps: Application and testing on a [11C]DPN study

• Published in: NeuroImage (Orlando, Fla.) Vol. 59; no. 3; pp. 2485 - 2493
• Main Authors: Rizzo, G., Turkheimer, F.E., Keihaninejad, S., Bose, S.K., Hammers, A., Bertoldo, A.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01-02-2012; Elsevier Limited
• Subjects: Algorithms; Atlases as Topic; Bayes Theorem; Bayesian estimation; Bias; Binding sites; Brain - anatomy & histology; Brain Mapping - methods; Calibration; Carbon Radioisotopes; Cluster Analysis; Diprenorphine; Estimates; Humans; Image Processing, Computer-Assisted; Kinetics; Magnetic Resonance Imaging; Methods; Models, Statistical; Nonlinear Dynamics; Normal Distribution; Parametric imaging; Positron emission tomography; Positron-Emission Tomography - methods; Radiopharmaceuticals; Reproducibility of Results; Studies; Parametric imaging; Positron emission tomography; Bayesian estimation
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2011.08.101

We propose a general approach to generate parametric maps. It consists in a multi-stage hierarchical scheme where, starting from the kinetic analysis of the whole brain, we then cascade the kinetic information to anatomical systems that are akin in terms of receptor densities, and then down to the voxel level. A-priori classes of voxels are generated either by anatomical atlas segmentation or by functional segmentation using unsupervised clustering. Kinetic properties are transmitted to the voxels in each class using maximum a posteriori (MAP) estimation method. We validate the novel method on a [11C]diprenorphine (DPN) test–retest data-set that represents a challenge to estimation given [11C]DPN's slow equilibration in tissue.
The estimated parametric maps of volume of distribution (VT) reflect the opioid receptor distributions known from previous [11C]DPN studies. When priors are derived from the anatomical atlas, there is an excellent agreement and strong correlation among voxel MAP and ROI results and excellent test–retest reliability for all subjects but one. Voxel level results did not change when priors were defined through unsupervised clustering.
This new method is fast (i.e. 15min per subject) and applied to [11C]DPN data achieves accurate quantification of VT as well as high quality VT images. Moreover, the way the priors are defined (i.e. using an anatomical atlas or unsupervised clustering) does not affect the estimates.
► We developed a general approach to generate precise and accurate parametric maps. ► The novel method uses a Maximum a Posteriori estimator. ► The priors are derived either by anatomical or functional segmentation. ► We validate the algorithm on a test–retest data-sets of [11C]diprenorphine (DPN). ► This new method is fast and robust. It achieves accurate parameter quantification.